System and method of predicting failures

ABSTRACT

A system and method for prediction of failures and optimization, that can provide solution available for unsupervised learning models based on limited data that can predict different types of failure and pre-failure instances. The solution provides improvement upon previous methods of labelling by marking certain days data ahead of failure as belonging to failure data which will result in reduction of noisy data and improves good working condition data. The present invention helps with improved data quality due to labelling as the proposed method models complex distributions of feature vectors accurately and are better at finding deviations from normal data distribution which is used for detecting failures. The novel solution help to analyse and categorise the type of failures for PC Pumps currently deployed in CBM Fields for which failure days in advance can be predicted.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a National Stage of International Application No.PCT/IB2021/060968, filed on Nov. 25, 2021, which claims priority toIndian Patent Application No. 202021052165, filed Nov. 30, 2020, thedisclosures of which are hereby incorporated by reference in theirentirety.

FIELD OF INVENTION

The present invention relates generally to data mining, deep learningbased supervised Machine Learning for sparse labelled data availabilityscenarios and more particular to predicting failures and optimizationfor progressive cavity pumps used at Coal Based Methane Gas Wells andalso prediction for various application other failures in domain ofpetrochemical agriculture, health and other allied industry.

BACKGROUND

The following description of related art is intended to providebackground information pertaining to the field of the disclosure. Thissection may include certain aspects of the art that may be related tovarious features of the present disclosure. However, it should beappreciated that this section be used only to enhance the understandingof the reader with respect to the present disclosure, and not asadmissions of prior art.

Coalbed methane (CBM or coal-bed methane), is a form of natural gasextracted from coal bedsand is an important source of energy in manycountries including India. The term refers to methane adsorbed into thesolid matrix of the coal. It is called ‘sweet gas’ because of its lackof hydrogen sulphide. The presence of this gas is well known from itsoccurrence in underground coal mining. Coalbed methane is distinct froma typical sandstone or other conventional gas reservoir, as the methaneis stored within the coal by a process called adsorption. The methane isin a near-liquid state, lining the inside of pores within the coal(called the matrix). The open fractures in the coal (called the cleats)can also contain free gas or can be saturated with water.

To extract the gas, a steel-encased hole is drilled into the coal seam100 to 1,500 metres (330 to 4,920 ft) below ground. As the pressurewithin the coal seam declines due to natural production or the pumpingof water from the coalbed, both gas and produced water come to thesurface through tubing as shown in the FIG. 1A. Then the gas is sent toa compressor station and into natural gas pipelines. Both gas andproduced water are carried by a progressive cavity pump (PCP). The PCPis a type of positive displacement pump and is also known as aprogressing cavity pump, progg cavity pump, eccentric screw pump orcavity pump. The PCP consists of a stator and a rotor, while the PCPsystem consists of PCP along with all the surface equipment like drivehead and sub-surface equipment like Tubing, Sucker rods, Tag Anchor/Noturn tool etc. It transfers fluid by means of the progress, through thepump, of a sequence of small, fixed shape, discrete cavities, as itsrotor is turned. This leads to the volumetric flow rate beingproportional to the rotation rate (bidirectionally) and to low levels ofshearing being applied to the pumped fluid. The PCP has its applicationin various sectors such as Food and drink pumping, Oil pumping, Coalslurry pumping, Sewage and sludge pumping, Viscous chemical pumping,Stormflow screening, Downhole mud motors in oilfield directionaldrilling (it reverses the process, turning the hydraulic into mechanicalpower), Limited energy well water pumping, etc. Artificial lift is usedto lower the producing bottomhole pressure (BHP) on the formation toobtain a higher production rate from the well. This can be done with apositive-displacement downhole pump, such as a beam pump, a progressivecavity pump (PCP) or a downhole centrifugal pump, to lower the bottomhole pressure in the Reservoir.

The initial operational goal of all CBM wells is to de-pressure thereservoir by continuously producing water at a low flowing bottom holepressure. The PCP's are chosen because of the following key operationalbenefits—

-   -   Solids handling capability of PCP.    -   Capability to tolerate high percentages of free gas.    -   Low maintenance.    -   Low cost.

Gas is being produced by dewatering the CBM wells with the help ofartificial lift system. Progressive cavity pump (PCP) System is used asthe artificial lift in all wells. For optimization of gas production, itis very important to minimize the downtime of PCP operation.

However, PCP pumps may fail. The failure of the PCP pumps at the CBMwells accounts for over 70-80% of all wellsite failures and causesapproximately 40 days of downtime per well annually as per thestatistical reports. The String integrity issues (Sucker rod and/orTubing string failure) have been the most prominent cause of unplannedwork overs, which, if prevented by improved monitoring, can lead toimproved run times and lesser cost. In a CBM Field PCP system failuresare the major source of downtime in well production life and enablingearly prediction will considerably reduce downtime.

These failures result in a high total cost-per-unit. Hence, predictingfailures accurately is the prime important goal to achieve consistentproduction and predict pump failures in advance to optimize maintenancecrew deployment and replacement part preparation, thereby reducingcosts.

The type of String integrity failures in PCP System in CBM Field willfall under one of the below categorizations:—

-   -   1. Tubing Puncture    -   2. Sucker rod Unscrew/Snap or Tubing Unscrew/Snap

The PCP pumps used at Coal Based Methane Gas Wells are subjected to wearand tear and subsequent failure of components or the well as wholeresults in operational loss and business. Either a components failure ormultiple components failure or accumulation of sand can causeoperational loss. Depending on cause of failure, type of failure (tubingintegrity, sand cleaning, pump failure, sand and pump, sucker rodintegrity etc.), the failures need to be labelled and defined also. In aCBM Field PCP system failures are the major source of downtime in wellproduction life and enabling early prediction will considerably reducedowntime.

The supervised Machine learning models can be used for identifying andpredicting failures. But, such supervised learning models requires datacorresponding to different types of failure and pre-failure instances.It may not be feasible to have data for all types of failure and modelsbuilt from limited data and hence predicting failures may not beaccurate for which data is not available. Currently, there are nosolutions available for unsupervised learning models based on limiteddata that can predict different types of failure and pre-failureinstances. There are also no solutions available that helps withimproved data quality due to labelling as the proposed method modelscomplex distributions of feature vectors accurately and are better atfinding deviations from normal data distribution which is used fordetecting failures. Further, there are no solution that improves uponprevious methods of labelling by marking certain days data ahead offailure as belonging to failure data which will result in reduction ofnoisy data and improves good working condition data. Another limitationin the current technology is, there is no solution to analyse andcategorize the type of failures for PC Pumps currently deployed in CBMFields for which failure days in advance can be predicted. Furthermore,there is no solution for a better optimal solution to increase theaccuracy of prediction and where false positive and false negativeshould be minimal.

There is, therefore, a requirement in the art for a methodology tooptimise production based on limited data that can predict differenttypes of failure and pre-failure instances associated with gasextraction components.

OBJECTS OF THE PRESENT DISCLOSURE

An object of the present invention is to provide method and system thatcan provide solution available for supervised learning models based onlimited data that can predict different types of failure and pre-failureinstances.

Another object of the present invention is to provide solution thatimproves upon previous methods of labelling by marking certain days dataahead of failure as belonging to failure data which will result inreduction of noisy data and improves good working condition data.

Another object of the present invention is to provide solution thathelps with improved data quality due to labelling as the proposed methodmodels complex distributions of feature vectors accurately and arebetter at finding deviations from normal data distribution which is usedfor detecting failures.

An object of the present invention is to provide method and system tointelligently identify solution to improve upon the approaches thataddresses the limited or non-availability of failure data.

Another object of the present invention is to provide solution that helpto analyse and categorise the type of failures for PC Pumps currentlydeployed in CBM Fields for which failure days in advance can bepredicted.

Another object of the present invention is to provide solution that helpwith the prediction of the failures in the Progressive Cavity Pump (PCP)used in Coal Bed Methane (CBM) wells for gas extraction.

Another object of the present invention is to provide solution that helpto analyse and categorize the type of failures for any similar equipmentfor which failure days in advance can be predicted.

Another object of the present invention is to provide a better optimalsolution to increase the accuracy of prediction and where false positiveand false negative should be minimal.

Another object of the present invention is to provide a solution thatcan cut down lease operating expense of equipments, decrease deferredproduction of gas, reduce non-productive time, alleviate hiringconstraints, improve cash flow in uncertain environment and providesustainable economic production, maximize reserves recovery, etc. bypredicting the failures of equipment.

Yet another object of the present invention is to mechanism to providesa seamless enhancement of prediction analysis to provide informativeoutput for precision and decision services on wireless network includingbut not limited to 5G/4G/3G/EV-Do/eHRPD capable technology.

Yet another object of the present invention is to mechanism to providesa seamless enhancement of prediction optimization analysis to provideinformative output for precision and decision services in the userdevices independent of whether the UE is 5G/4G/3G/EV-Do/eHRPD capabletechnology.

Another object of the present invention is to provide value-addedservices to explorers by predicting the operational challenge and savecost.

SUMMARY

This section is provided to introduce certain objects and aspects of thepresent invention in a simplified form that are further described belowin the detailed description. This summary is not intended to identifythe key features or the scope of the claimed subject matter.

In order to achieve the aforementioned objectives, in an aspect, thepresent invention provides a system and method for facilitatingprediction of wear and tear and subsequent failure of componentsassociated with gas extraction in a mining well. The system may includeone or more user equipment communicatively coupled to the mining wellfor gas extraction and one or more sensors coupled to one or more pumpsin the mining well. The one or more user equipment may further includeone or more processors that execute a set of executable instructionsthat are stored in a memory, upon which execution, the processor maycause the system to acquire a set of data packets from one or moresensors, by a data acquisition engine, where the set of data packets maybe received at any synchronous and asynchronous instances of time andextract a set of attributes, by a feature generation engine, from thesynchronised data packets. The feature generation engine maybeconfigured to generate features from the extracted set of attributesassociated with interpolation of the received data packets. Theprocessor may also cause the system to evaluate, by a generativeadaptive network (GAN) engine, a set of model parameters based on thegenerated features of the extracted set of attributes and based on theevaluation of the set of model parameters, predict, by a predictionengine, failures associated with the received set of data packets.

In an aspect, the present invention provides a method for facilitatingprediction of wear and tear and subsequent failure of componentsassociated with gas extraction in a mining well. The method may includethe steps of acquiring, by a data acquisition engine, a set of datapackets from one or more sensors, where the set of data packets may bereceived at any synchronous and asynchronous instances of time;extracting, by a feature generation engine, a set of attributes from theacquired set of data packets, where the feature generation engine may beconfigured to generate features from the extracted set of attributesassociated with interpolation of the acquired data packets; evaluating,by a generative adaptive network (GAN) engine, a set of model parametersbased on the generated features of the extracted set of attributes andbased on the evaluation of the set of model parameters, predict, by aprediction engine, failures associated with the received set of datapackets.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated herein, and constitutea part of this invention, illustrate exemplary embodiments of thedisclosed methods and systems in which like reference numerals refer tothe same parts throughout the different drawings. Components in thedrawings are not necessarily to scale, emphasis instead being placedupon clearly illustrating the principles of the present invention. Somedrawings may indicate the components using block diagrams and may notrepresent the internal circuitry of each component. It will beappreciated by those skilled in the art that invention of such drawingsincludes the invention of electrical components, electronic componentsor circuitry commonly used to implement such components.

FIG. 1A illustrates a coal bed methane well, in accordance with anembodiment of the present disclosure.

FIG. 1B illustrates a typical PCP installation, in accordance with anembodiment of the present disclosure.

FIG. 2A illustrates an exemplary network architecture (200) in which orwith which the system of the present disclosure can be implemented, inaccordance with an embodiment of the present disclosure.

FIG. 2B illustrates an exemplary representation (200) of system (110) ora centralized server (112), in accordance with an embodiment of thepresent disclosure.

FIG. 3A illustrates an exemplary representation system architecture ofuser equipment latched with different RATS, in accordance with anembodiment of the present disclosure.

FIG. 3B illustrates an exemplary representation depicting a userequipment architecture of system, in accordance with an embodiment ofthe present disclosure.

FIG. 4 illustrates exemplary method flow diagram (400) depicting amethod for prediction of failures, in accordance with an embodiment ofthe present disclosure.

FIG. 5A illustrates an exemplary representation system architecture ofGeneral Adaptive Network (GAN) Engine, in accordance with an embodimentof the present disclosure.

FIG. 5B illustrates an exemplary representation system architecture ofData labelling Engine, in accordance with an embodiment of the presentdisclosure.

FIG. 6A illustrates an exemplary representation system architecture ofGAN Training Engine, in accordance with an embodiment of the presentdisclosure.

FIG. 6B illustrates an exemplary representation of flow diagram fordetection of anomalies, in accordance with an embodiment of the presentdisclosure.

FIG. 6C illustrates an exemplary representation of a scattering plot ofsamples, in accordance with an embodiment of the present disclosure.

FIG. 7 illustrates an exemplary representation process flow diagram, inaccordance with an embodiment of the present disclosure.

FIG. 8 illustrates an exemplary representation system architecture ofPCP fault prediction engine, in accordance with an embodiment of thepresent disclosure.

BRIEF DESCRIPTION OF INVENTION

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofembodiments of the invention. However, it will be apparent that variousembodiments may be practiced without these specific details. The figuresand description are not intended to be restrictive.

Referring to FIG. 2A that illustrates an exemplary network architecture100 in which or with which system 106 of the present disclosure can beimplemented, in accordance with an embodiment of the present disclosure.As illustrated, the exemplary architecture 100 includes a modellingsystem 106 equipped with a machine learning prediction engine 218 (alsoreferred to as prediction engine 218 hereinafter) for facilitatingprediction of wear and tear and subsequent failure of componentsassociated with gas extraction in Coal Bed Methane (CBM) wells. Themodelling system 106 may be further coupled to one or more userequipment 102-1, 102-2, 102-3 . . . 102-n (collectively referred to asuser equipment 102 and individually referred to as user equipment 102hereinafter) communicatively coupled to Coal Bed Methane (CBM) wells forgas extraction. The system 106 may also be coupled to one or moresensors coupled to progressive cavity pump (PCP) in Coal Bed Methane(CBM) wells 110 (interchangeably referred to as one or more sensors 110or sensors 110) through a network 104 to send and receive sensor datafrom the one or more sensors to the modelling system for processing.

In accordance with an embodiment and as illustrated in FIG. 2A, thearchitecture can enable prediction of failures associated with anydamage or failure of components in the PCP in CBM wells, in response towhich anomalies and failures are predicted. The failures are evencategorised into different labels for an optimized and hassle-free gasextraction from the wells. Failures of different components may besensed by the one or more sensors 110 and the set of data packets may besent to the modelling system 106 coupled to the user equipment 102.Using the prediction engine 218 in the system (106), anomalies andfailures can be evaluated based on received set of data packets from theone or more sensors 110.

In an embodiment, information related to failures may be accessed usingthe user equipment via set of instructions residing on any operatingsystem, including but not limited to, Android™, iOS™, and the like. Inan embodiment, the one or more user equipment may be any smart computingdevices and correspond to any electrical, electronic, electro-mechanicalor an equipment or a combination of one or more of the above devices.Smart computing devices may include, but not limited to, a mobile phone,smart phone, IoT Devices, virtual reality (VR) devices, augmentedreality (AR) devices, pager, laptop, a general-purpose computer,desktop, personal digital assistant, tablet computer, mainframecomputer, or any other computing device as may be obvious to a personskilled in the art. In general, a smart computing device is a digital,user-configured, computer networked device that can operateautonomously. A smart computing device is one of the appropriate systemsfor storing data and other private/sensitive information. The saiddevice operates at all the seven levels of ISO reference model, but theprimary function is related to the application layer along with thenetwork, session and presentation layer with any additional features ofa touch screen, apps ecosystem, physical and biometric security, and thelike.

The smart computing devices or the user equipment may include smartphones having mobility wireless cellular connectivity device that mayallow end-users to use services on 2G, 3G, 4G or 5G mobile broadbandInternet connections with an advanced mobile operating system whichcombines features of a personal computer operating system with otherfeatures useful for mobile or handheld use. The smartphones can accessthe Internet, have a touch screen user interface, can run third-partyapps including the capability of hosting online applications, musicplayers and are camera phones possessing high-speed mobile broadband4G/5G LTE internet with video calling, hotspot functionality, motionsensors, mobile payment mechanisms and enhanced security features withalarm and alert in emergencies. Mobility devices may includesmartphones, wearable devices, smart-watches, smart bands, wearableaugmented devices, etc. For the sake of specificity, we will refer tothe mobility device to both feature phone and smartphones in thisdisclosure but will not limit the scope of the disclosure and may extendto any mobility device in implementing the technical solutions. Theabove smart devices including the smartphone as well as the featurephone including IoT devices enable the communication on the devices.

The set of data packets are transmitted by the sensors 110 through thenetwork 104. In an exemplary embodiment which is an example but not alimitation, the network 104 may be Evolved Universal Terrestrial RadioAccess (E-UTRA) which is a radio access network standard meant to be areplacement of the UMTS and HSDPA/HSUPA technologies specified in 3GPPreleases 5 and beyond. Unlike HSPA, LTE's E-UTRA is an entirely new airinterface system, unrelated to and incompatible with W-CDMA. It provideshigher data rates, lower latency and is optimized for packet data. Theearlier UTRAN is the radio access network (RAN) was defined as a part ofthe Universal Mobile Telecommunications System (UMTS), athird-generation (3G) mobile phone technology supported by the 3rdGeneration Partnership Project (3GPP). The UMTS, which is the successorto Global System for Mobile Communications (GSM) technologies, currentlysupports various air interface standards, such as Wideband-Code DivisionMultiple Access (W-CDMA), Time Division-Code Division Multiple Access(TD-CDMA), and Time Division-Synchronous Code Division Multiple Access(TD-SCDMA). The UMTS also supports enhanced 3G data communicationsprotocols, such as High-Speed Packet Access (HSPA), which provideshigher data transfer speeds and capacity to associated UMTS networks. Asthe demand for mobile data and voice access continues to increase,research and development continue to advance the technologies not onlyto meet the growing demand for access, but to advance and enhance theuser experience with user device. Some of the technologies that haveevolved starting GSM/EDGE, UMTS/HSPA, CDMA2000/EV-DO and TD-SCDMA radiointerfaces with the 3GPP Release 8, e-UTRA is designed to provide asingle evolution path for providing increases in data speeds, andspectral efficiency, and allowing the provision of more functionality.

As certain way of example and not by way of limitation, the presentdisclosure may use a new technology NB-IoT in release 13 for 3GPP. Thelow-end IoT applications can be met with this technology. It has takenefforts to address IoT markets with completion of standardization onNB-IoT. The NB-IoT technology has been implemented in licensed bands.The licensed bands of LTE are used for exploiting this technology. Thistechnology makes use of a minimum system bandwidth of 180 kHz i.e., onePRB (Physical Resource Block) is allocated for this technology. TheNB-IoT can be seen as a separate RAT (Radio Access Technology). TheNB-IoT can be deployed in 3 modes as: “in-band”, “guard band” and“standalone”. In the “in-band” operation, resource blocks present withinLTE carrier are used. There are specific resource blocks reserved forsynchronization of LTE signals which are not used for NB-IoT. In “guardband” operation, resource blocks between LTE carriers that are notutilized by any operator are used. In “standalone” operation, GSMfrequencies are used, or possibly unused LTE bands are used. Release 13contains important refinements like discontinuous reception (eDRX) andpower save mode. The PSM (Power Save Mode) ensures battery longevity inrelease 12 and is completed by eDRX for devices that need to receivedata more frequently.

FIG. 2B with reference to FIG. 2A, illustrates an exemplaryrepresentation of modelling system 106/user equipment 102 forfacilitating prediction of failures associated with gas extractionsystems, in accordance with an embodiment of the present disclosure. Inan aspect, the system (106)/user equipment 102 may comprise one or moreprocessor(s) 202. The one or more processor(s) 202 may be implemented asone or more microprocessors, microcomputers, microcontrollers, digitalsignal processors, baseband digital processors, central processingunits, logic circuitries, and/or any devices that process data based onoperational instructions. Among other capabilities, the one or moreprocessor(s) 202 may be configured to fetch and executecomputer-readable instructions stored in a memory 204 of the system 106.The memory 204 may be configured to store one or more computer-readableinstructions or routines in a non-transitory computer readable storagemedium, which may be fetched and executed to create or share datapackets over a network service. The memory 206 may comprise anynon-transitory storage device including, for example, volatile memorysuch as RAM, or non-volatile memory such as EPROM, flash memory, and thelike.

In an embodiment, the modelling system 106/user equipment (102) mayinclude an interface(s) 204. The interface(s) 204 may comprise a varietyof interfaces, for example, interfaces for data input and outputdevices, referred to as I/O devices, storage devices, and the like. Theinterface(s) 204 may facilitate communication of the modelling system106. The interface(s) 204 may also provide a communication pathway forone or more components of the user equipment 102. Examples of suchcomponents include, but are not limited to, processing engine(s) 208 anda database 210.

The processing engine(s) (208) may be implemented as a combination ofhardware and programming (for example, programmable instructions) toimplement one or more functionalities of the processing engine(s) (208).In examples described herein, such combinations of hardware andprogramming may be implemented in several different ways. For example,the programming for the processing engine(s) 208 may be processorexecutable instructions stored on a non-transitory machine-readablestorage medium and the hardware for the processing engine(s) 208 maycomprise a processing resource (for example, one or more processors), toexecute such instructions. In the present examples, the machine-readablestorage medium may store instructions that, when executed by theprocessing resource, implement the processing engine(s) (208). In suchexamples, the system 106/user equipment 102 may comprise themachine-readable storage medium storing the instructions and theprocessing resource to execute the instructions, or the machine-readablestorage medium may be separate but accessible to the system 106/userequipment 102 and the processing resource. In other examples, theprocessing engine(s) 208 may be implemented by electronic circuitry.

The processing engine 208 may include one or more engines selected fromany of a data acquisition engine 212, a feature generation engine 214,Generative Adaptive Network (GAN) engine 216, prediction engine 218 andother engines (220). In an embodiment, the data acquisition engine 212may enable acquire a set of data packets from one or more sensors 110.The set of data packets may be received at any synchronous andasynchronous instances of time which are then converted to synchronousdata packets by the data acquisition engine 212. In an embodiment, thefeature generation engine 214 may enable to extract a set of attributes,by a feature generation engine, from the synchronised data packets. Thefeature generation engine may be configured to generate features fromthe extracted set of attributes associated with interpolation of thereceived data packets. The generative adaptive network (GAN) engine 216may be configured to evaluate a set of model parameters based on thegenerated features of the extracted set of attributes and based on theevaluation of the set of model parameters, the prediction engine 218 maydetect anomalies associated with the data packets and predict failuresassociated with the received set of data packets.

In an embodiment, the GAN engine 216 may include machine learningtechniques where given a training set, this technique learns to generatenew data with the same statistics as the training set. For example, aGAN trained on data can generate new events that look at leastsuperficially authentic to human observers, having many realisticcharacteristics. Though originally proposed as a form of generativemodel for supervised learning, GANs have also proven useful forsemi-supervised learning, unsupervised learning, and reinforcementlearning. In an exemplary embodiment, the GAN engine can be configuredto analyse each set of data packets received from the sensors.

In an embodiment, the prediction engine (218) may include machinelearning methodologies using Gaussian process. The Gaussian process is astochastic process (a collection of random variables indexed by time orspace), such that every finite collection of those random variables hasa multivariate normal distribution, i.e., every finite linearcombination of them is normally distributed. The distribution of aGaussian process is the joint distribution of all those (infinitelymany) random variables, and as such, it is a distribution over functionswith a continuous domain, e.g., time or space. A machine-learningalgorithm that involves a Gaussian process uses lazy learning and ameasure of the similarity between points (the kernel function) topredict the value for an unseen point from training data. The predictionis not just an estimate for that point, but also has uncertaintyinformation—it is a one-dimensional Gaussian distribution (which is themarginal distribution at that point). For multi-output predictions,multivariate Gaussian processes are used, for which the multivariateGaussian distribution is the marginal distribution at each point. Theprediction engine 218 also may include artificial intelligence,cognitive modelling, and neural networks are information processingparadigms inspired by the way biological neural systems process data.Artificial intelligence and cognitive modeling try to simulate someproperties of biological neural networks. In the artificial intelligencefield, artificial neural networks have been applied successfully tospeech recognition, image analysis and adaptive control. A neuralnetwork (NN), in the case of artificial neurons called artificial neuralnetwork (ANN) or simulated neural network (SNN), is an interconnectedgroup of natural or artificial neurons that uses a mathematical orcomputational model for information processing based on a connectionistapproach to computation. In most cases, an ANN is an adaptive systemthat changes its structure based on external or internal informationthat flows through the network. In more practical terms neural networksare non-linear statistical data modelling or decision-making tools. Theycan be used to model complex relationships between inputs and outputs orto find patterns in data. Further, the prediction engine 218 may involveoptimization of the model parameters evaluated by the GAN engine 216.Optimization is the process of the determination of a set of values forthe design parameters that solves a maximization or minimizationfunction of a set of objectives derived from the quantities of interest(QOIs).The optimization of a complex system involves the determinationof optimum values for a set of design parameters in order to meet aspecific set of objectives based concerning the QOIs in which the designparameters are a subset of the input parameters and the QOIs aredetermined from the output parameters. The system can be an experimentor a computational model. Particularly, when the parameter space islarge, optimization necessitates a significant number of executions ofthe system to obtain a desired solution in tolerance limits.

FIG. 3A illustrates an exemplary representation system architecture ofuser equipment latched with different radio access technology systems(RATS), in accordance with an embodiment of the present disclosure.

As illustrated, in an embodiment, FIG. 3A depicts system architecture ofa UE/IoT concurrently latched to LTE as well as legacy (UMTS/GSM/LTE) or5G-NR operator. In an embodiment, as illustrated in FIG. 3A, a userequipment 102 (UE) may be communicatively coupled to eNodeB 304 andNodeB or Base station (BTS) 308. The eNodeB 304 may be further coupledto an LTE network 306 while BTS 308 may be associated with any or acombination of UTRAN and GSM Network 310.

FIG. 3B illustrates an exemplary representation depicting a userequipment architecture of system, in accordance with an embodiment ofthe present disclosure.

In another embodiment, the below schematic diagram depicts a simplifiedblock representation of a UE 102 (Smartphone/feature phone/any othercommunicating device) in FIG. 3A. FIG. 3B illustrates a preferredembodiment of the present disclosure which encompasses a high-levelarchitecture of a system 300 for availing at least one service by theuser equipment 102. The system 300 may comprise the user equipment 102and the subscriber identity engine (SIM) 320 configured inside the userequipment 102 for providing various functionalities in accordance withthe present disclosure. The user equipment 102 further may comprise aplurality of subsystems [312, 312A, 320B, 312C, 304, 314, 306 and 316],wherein said subsystems [312, 312A, 320B, 312C, 304, 314, 306 and 316]may include, but not limiting to, a modem subsystem 312 with a BasebandDSP processor 312C and a plurality of radio interfaces 312A. The userequipment 102 may further include a cellular radio 102Btransmission/reception radio frequency (RF) connected to the antenna 308for receiving and transmitting wireless services such as VoIP andInternet/Intranet services. Also, the user equipment 102 may comprise anapplication processor 314 a memory subsystem 306, a power subsystem 316and an external I/O interfaces subsystem 304. The present disclosurefurther encompasses that the subscriber identity engine 320 may comprisea processor 320B, an I/O interface 320A, a RAM temporary storage 320C,an EEPROM/Non-volatile Memory (NVM) [320D] and a SIM file system [320E].Further, the EEPROM/Non-Volatile Memory (NVM) [320D] may consist of anoperating system code, a code of other SIM applications and the AutoIMSI Switch SIM application. The SIM file system [320E] and USIMapplication may contain elementary files and location parameters such asEFLOCI (Location Information), EFPSLOCI (PS Location Information),EFEPSLOCI (PS Location Information) and various application-specificfiles used by SIM applications running on the subscriber identity engine[320] along with a plurality of context and configuration files of theAuto IMSI Switch SIM application.

FIG. 4 illustrates exemplary method flow diagram (400) depicting amethod for prediction of failures, in accordance with an embodiment ofthe present disclosure.

At step 402, the method includes the step of acquiring, by the dataacquisition engine, a set of data packets from one or more sensors,wherein the set of data packets are received at any synchronous andasynchronous instances of time and at step 404, the method may includeextracting, by a feature generation engine, a set of attributes from theacquired set of data packets, wherein the feature generation engine isconfigured to generate features from the extracted set of attributesassociated with interpolation of the acquired data packets.

Further at step 406, the method may include the step of evaluating, by agenerative adaptive network (GAN) engine, a set of model parametersbased on the generated features of the extracted set of attributes andbased on the evaluation of the set of model parameters, the method mayinclude a step at 408 predicting, by a prediction engine, failuresassociated with the received set of data packets.

FIG. 5A illustrates an exemplary representation system architecture ofGeneral Adaptive Network (GAN) Engine, in accordance with an embodimentof the present disclosure.

The above system takes sensor input 502-1, 502-2 and 502-3 as raw datafor data acquisition 504, cleaning 506 and labelling 510 and feed thefeature generation 508 data to the GAN optimizer engine 512 processed inparallel to provide the output for the optimized failure predictionsolution. The system comprises of scenario, dynamic data and metadatatables as input and generates optimized failure prediction solution(dashboards, plots and CSV files) as output for the stakeholders toanalyze and take decisions.

The above system has broadly following steps—

-   -   Feature extraction from time series of different sensors    -   Data labelling process    -   Training of GAN Models    -   Inference pipeline

A detailed description of each of the components are presented in thefollowing section.

Data Acquisition 504 and cleaning 506 (Resampling and Interpolation):Each sensor emits data at a specific time interval. The time interval ofdata emission is different for each sensor. Apart from the timeinterval, instances at which data is emitted from sensors may not besynchronised. The asynchronous and irregular time interval data fromdifferent sensors is interpolated to a synchronous and regular timeinterval data. Let N be the number of sensors emitting data. Afterresampling and interpolation, an observation is set at each time instantto set of values {r_(i) ^(t)}_(i=1) ^(N), where each dimensioncorresponds to a interpolated or original value from a sensor and itsvector representation is r^(t).

Features Generation 508 (Derived Features): Few features are derived byperforming computations on observations of particular sensor from theinterpolation step. Following types of computations are generallyperformed on the window of observations from sensor, depending on theperformance improvements provided by those features.

-   -   1. Statistics like mean, median, kurtosis etc. on a window of        observations. Computation of statistics results in a scalar        value for each window of observations.    -   2. Frequency domain transformations like Fourier transform or        time frequency transformations like wavelets etc. Output from        these transformations will result in a vector of values when        applied on a window of observations.    -   3. Dimensionality reduction like Principal Components Analysis        can be applied on features extracted from steps 1 and 2 to        reduce the dimensionality of feature vector to improve        computational performance or robustness to noise.

Let us assume that there are M number of derived features usingtransformations on different features mentioned in step 1 and step 2.The derived features values are appended to the feature values fromresampling and interpolation step which we call as derived featurevector. This will result in a derived feature vector with values

{x

_i^t}

_(i=1)^(M+N) where each dimension corresponds to a interpolated or rawsignal value from a sensor or derived feature value and its vectorrepresentation is x^t.

FIG. 5B illustrates an exemplary representation system architecture ofData labelling Engine, in accordance with an embodiment of the presentdisclosure.

In an embodiment, for each gas well, workover start date (ws^(start))and workover end date (ws^(end)) are available as a csv file which areused to mark each observation computed in feature extraction process.All observations between workover start date and workover end date aremarked as failure condition data. Also, a window (W days) ofobservations before workover start date are also marked as failure inorder to enable ahead failure prediction. All other observations outsideof the window (ws^(start)−W, ws^(end)) are marked as observationsbelonging to good condition data. After labelling process we have datain the format {x^(t), y^(t)}, where y^(t) takes value either good orfailure.

FIG. 6A illustrates an exemplary representation system architecture ofGAN Training Engine, in accordance with an embodiment of the presentdisclosure.

As illustrated, the GAN training flow is depicted in terms of a boldarrow. The bold arrow above shows the training process flow and thedotted arrow corresponds to the testing flow. After the training isdone, the model parameters (weights) of Generator and Discriminator maybe stored in a binary format. During the inference process, theseweights may be loaded and used in the model for predictions.

During the training process as depicted below in FIG. 6A, GAN model isused for modelling variability of good working condition of gas wells. AGAN consists of adversarial engines, a generator G and a discriminatorD. The generator G learns a distribution p_(g) over data x via a mappingG(z) of samples z, 1D vectors of uniformly distributed input noisesampled from latent space, to feature space. In this setting, thenetwork architecture is a standard neural network decoder. Let length ofthe vector z be L. Here, Different values of L_(z) will be explored andchose the one which results in best performance of the model.

Discriminator D is a neural network that maps a derived feature vectorto single scalar value D(.). The discriminator output D(.) can beinterpreted as probability that the given input to the discriminator Dwas a feature vector from training data belonging to good workingcondition of the well or generated G(z) by the generator G. D and G aresimultaneously optimized through the below two player minimax game withvalue function V(G, D).

${\min\limits_{G}\max\limits_{D}{V\left( {D,G} \right)}} = {{E_{x\sim{p_{data}(x)}}\left\lbrack {\log{D(x)}} \right\rbrack} + {E_{z\sim{p_{z}(z)}}\left\lbrack {\log\left( {1 - {D(z)}} \right)} \right\rbrack}}$

The discriminator is trained to maximize the probability of assigninggood working condition training examples the “good” and samples fromp_(g) the “failure” label. The generator is simultaneously trained tofool D via minimizing V(G)=log(1−D(G(z))) which is equivalent tomaximizing V(G)=D(G(z)). During adversarial training the generatorimproves in generating derived features in good condition and thediscriminator progresses in correctly identifying good and not goodfeatures.

The Generator and Discriminator networks are trained using backpropagation of gradients of loss function w.r.t different parameters inGenerator G and discriminator D network. Generator and Discriminatorweights are updated iteratively in training engine. In each iterationgenerator and discriminator weights are updated. While updating weightsof a generator discriminator weights are kept constant and whileupdating the discriminator weights generator weights are kept constant.Number of iterations for which generator and discriminator weightupdates happen is referred to as Number of epochs (N_(epoch)).

Discriminator: The discriminator is a binary classifier that identifiesif a given sample corresponds to a normal sample or a failure sample.The samples are multi-dimensional. The terms of the variable space isdifficult to visualize completely and uses an approximation usingPrincipal Component Analysis (PCA) to reduce the dimensionality to twofor visualization purposes.

ZEstimator: New Feature to Latent Space: When adversarial training iscompleted, the generator has learned the mapping G(z) from latent spacerepresentations z to feature space of good working condition x of theCBM well. But, GANs do not automatically provide inverse mapping μ(x)from feature space to latent space. The latent space has smoothtransitions, so sampling two points close in the latent space generatestwo similar derived features. Given a query feature x, a point z, in thelatent space that corresponds to feature G(z) that is similar to queryfeature vector x. To find the best z, z₁ is randomly sampled from thelatent space distribution and fed into the generator to get a generatedderived feature vector G(z₁). Based on the generated derived featurevector G(z₁) a loss function is defined, which provides gradients forthe update of coefficients of z₁ resulting in an updated position inlatent space z₂. In order to find the most similar image G(z_(Γ)), thelocation of z in the latent space is optimized in an iterative processvia γ=1, 2, 3, . . . , Γ back propagation steps.

A loss function for mapping a new derived feature to the latent spacethat comprises two components, a residual loss and a discriminationloss. The residual loss enforces the similarity between generatedfeature vector G(z_(Γ)) and query feature vector. The Discriminativeloss enforces the generated feature vector G(z_(Γ)) to lie on thelearned manifold. Therefore, both components of GAN are utilized toadapt the coefficients of z, via back propagation.

Residual Loss: The residual loss measures the visual dissimilaritybetween query feature vector x and generated feature vector G(z_(γ)) inthe feature space and is defined by

L _(R)(z _(γ))=Σ|x−G(z _(γ))|

Discrimination Loss:

L _(D)(z _(γ))=Σ|f(x)−f(G(z _(γ)))|

For the mapping to latent space, the overall loss is defined as weightedsum of both components.

L _(D)(z ₆₅ )=(1−λ)L _(R)(z _(γ))+λL _(D)(z _(γ))

Only, the coefficients of z are adapted via back propagation. Thetrained parameters of the generator and discriminator are kept fixed.

FIG. 6B illustrates an exemplary representation of flow diagram fordetection of anomalies, in accordance with an embodiment of the presentdisclosure.

In an exemplary embodiment, during anomaly identification in new data,the new query feature vector is evaluated as belonging to normal orfailure scenario as depicted in the FIG. 6B. The Loss function used formapping to the latent space, evaluates in every update iteration γ thecompatibility of generated feature vector G(z_(γ)) with feature vectorsseen during adversarial training. Thus, an anomaly score, whichexpresses the fit of a query feature vector x to the model of goodfeature vector can be derived from the mapping loss function.

A(x)=(1−λ)R(x)+λD(x) where the residual score R(x) and discriminatorscore D(x) are defined by the residual loss L_(R)(z_(Γ)) and thediscriminator loss L_(D)(z_(γ)) at the last Γ^(th) update iteration ofthe mapping procedure to the latent space. The model yields a largeanomaly score for A(x) for failure feature vector, whereas a smallanomaly score means that a very similar feature vector was already seenduring training. The anomaly score A(x) for vector-based failuredetection. Additionally, the residual vector is used for theidentification of reasons of failure based on the dimensions along whichresidual is high.

FIG. 6C illustrates an exemplary representation of a scattering plot ofsamples, in accordance with an embodiment of the present disclosure.

FIG. 7 illustrates an exemplary representation process flow diagram, inaccordance with an embodiment of the present disclosure.

As illustrated, in an exemplary embodiment, the working steps of thepredictive GMS Systems (GMS) optimizer engine with other engines/systemsor subsystems may be provided below.

At step 702 acquire the data: Each sensor emits data at a specific timeinterval. The time interval of data emission maybe different for eachsensor. At step 704, analyse the data and clean the date to meet theinput requirements. There can be noise or missing values in the data.Noisy data points are discovered based on the limits in which signalvalues should lie. After identifying noisy and missing data instances,interpolation is used to estimate the missing values.

Further at step 706, if the data is not synchronised is yes, then atstep 708, synchronise the data if not synchronised. Apart from the timeinterval, instances at which data is emitted from sensors may not besynchronised. The asynchronous and irregular time interval data fromdifferent sensors is interpolated to a synchronous and regular timeinterval data. Let N be the number of sensors emitting data. Afterresampling and interpolation, an observation is set at each time instantto set of values {r_(i) ^(t)}_(i=1) ^(N), where each dimensioncorresponds to a interpolated or original value from a sensor and itsvector representation is r^(t).

Furthermore, at step 710 Feature Generation takes place if synchronised,to create a new feature generation, i.e., the raw and derived datacriteria is met. Few features may be derived by performing computationson observations of particular sensor from the interpolation step.Following types of computations are generally performed on the window ofobservations from sensor, depending on the performance improvementsprovided by those features. At step 712 apply the GAN module on thegenerated data and at step 714 apply observation and aggregation ofscores. Furthermore, at step 716, predict the types and chances offailures.

In an exemplary embodiment, by way of example and not as limitation,different models may be evaluated by comparing metrics recall rate,precision and accuracy. Recall Rate—Recall rate is the ratio of numberof failures that were detected correctly and total number of failuresthat actually happened.

Table I shows an example GAN engine training in accordance with anembodiment of the present disclosure.

Number Weight for Latent of Epochs Discriminator Dimension (N_(epoch))Function (λ) (L_(z)) Precision Recall Accuracy 100 0.1 6 0.99 0.96 0.99100 0.1 6 0.98 0.95 0.98 100 0.1 4 0.98 0.94 0.94 100 0.1 4 0.97 0.930.92 100 0.3 6 0.98 0.95 0.98 100 0.3 6 0.97 0.94 0.97 100 0.3 4 0.960.93 0.95 100 0.3 4 0.95 0.92 0.93

For example, if there are N_(F) failures and GAN mode is able to detectN_(DC) then recall rate=N_(DC)/N_(F).

Precision—It is ratio of number of failures detected correctly and Totalnumber of failures detected. For example, GAN models detects that N_(DF)failures are going to occur and only N_(DC) failures actually occurred.In this case accuracy=N_(DC)/N_(DF).

True Positive—A failure being detected as failure is called a TruePositive, True Negative—Normal working condition being detected asnormal working condition is called a True Negative. Accuracy—Accuracy isratio of sum of true positives and true negative and Total number ofdecisions made. In the proposed above embodiments for the process of theGMS platform architecture for the prediction optimization, during GANmodel training, the following set of parameters are explored to find amodel with best performance. The numbers in the Table I may beindicative only and may be replaced with numbers from experiment.

FIG. 8 illustrates an exemplary representation system architecture ofPCP fault prediction engine, in accordance with an embodiment of thepresent disclosure.

In an exemplary embodiment, a sample application solution of the aboveGMS Engine may involve the prediction of the failures in the ProgressiveCavity Pump (PCP) used in Coal Bed Methane (CBM) wells for gasextraction. A forward-looking prediction of the failure signals wouldresult in an increase in the overall operational efficiency and help inthe planning of maintenance schedules to take preventive actions andreduce downtime and work over cost. The failure in PCP can happen due tosand accumulation, water accumulation and tubing puncture. Theseoperational events affect the stator, rotor, tubing rod, internal andthe external casing. Following is a subset of the sensors (IoT UEs) usedin measuring the parameters during the operation of the PCP: currentsensor 804, torque sensor 802, tubing pressure sensor 806, annular flowrate sensor 808, rpm sensor 810, gas flow rate sensor 810 and the waterflow rate sensor 810. The following system presents a detailed flow ofthe GMS engine to analyse the CBM failure prediction use-case.

CBM Data Acquisition Module 812: The data acquisition engineimports/acquires data from several sensors and stores it in the stagingdata objects. These data objects may be managed and partitioned in adistributed environment for data processing. The sensors may record dataat different time frequency levels. This data is also termed as raw databecause the data stored here is not processed yet.

CBM Time Series Data Processing module 814: The data processing stepencompasses most of processing of raw data into a model-consumable form.It involves filling missing values, reduction of noise, cleaning data interms of improving the quality and then synchronising the data to bringa temporal consistency.

CBM GAN Model 816: The CBM GAN model may include of all the processesmentioned in the above sections. It may include a generator,discrimination, z-estimator and residual and discriminator losscalculations.

CBM Prediction Module 818: The CBM prediction module 818 may includeaggregator and threshold systems those predict the type of failure 820and the chances of failure 832. The type of failure may be determinedbased on a ranking of the probability of all the types of failures. Thechances of failure may be a number between 0 and 1.0. A high valueindicates that the chances of failure is high.

The foregoing examples of the related art and limitations relatedtherewith are intended to be illustrative and not exclusive. Otherlimitations of the related art will become apparent to those of skill inthe art upon a reading of the specification and a study of the figures.Also, the limitation is not subject to the Coal Bed Methane industryequipments as this is only an illustrative example, this may beapplicable to any similar industry where the predicting failures problemin heavy equipment exists.

In another embodiment, the solution can be used to predict the failure,identify anomalies and monitor the health of devices and any other heavyor light equipments. The sensors attached to the device or equipmentsshall measure the operational parameters in a time sequenced manner.However, in the physical world the following is the quality of dataacquired by the systems.

ADVANTAGES OF THE PRESENT DISCLOSURE

The present disclosure provides for a system and method that facilitatessupervised learning models based on limited data that can predictdifferent types of failure and pre-failure instances.

The present disclosure provides for a solution that improves uponprevious methods of labelling by marking certain days data ahead offailure as belonging to failure data which will result in reduction ofnoisy data and improves good working condition data.

The present disclosure provides for a solution to analyse and categorisethe type of failures for PC Pumps currently deployed in CBM Fields forwhich failure days in advance can be predicted. The present disclosureprovides for a solution that helps with the prediction of the failuresin the Progressive Cavity Pump (PCP) used in Coal Bed Methane (CBM)wells for gas extraction.

The present disclosure provides for a solution that helps to analyse andcategorize the type of failures for any similar equipment for whichfailure days in advance can be predicted.

The present disclosure provides for a better optimal solution toincrease the accuracy of prediction and where false positive and falsenegative should be minimal.

The present disclosure provides for a solution that can cut down leaseoperating expense of equipments, decrease deferred production of gas,reduce non-productive time, alleviate hiring constraints, improve cashflow in uncertain environment and provide sustainable economicproduction, maximize reserves recovery, etc. by predicting the failuresof equipment.

The present disclosure provides for a mechanism that facilitates aseamless enhancement of prediction analysis to provide informativeoutput for precision and decision services on wireless network includingbut not limited to 5G/4G/3G/EV-Do/eHRPD capable technology.

The present disclosure provides for a mechanism that facilitates aseamless enhancement of prediction optimization analysis to provideinformative output for precision and decision services in the userdevices independent of whether the UE is 5G/4G/3G/EV-Do/eHRPD capabletechnology.

The present disclosure provides value-added services to explorers bypredicting the operational challenge and save cost.

We claim:
 1. A system for facilitating prediction of wear and tear andsubsequent failure of components associated with gas extraction in amining well, said system comprising: one or more user equipmentcommunicatively coupled to Coal Bed Methane (CBM) wells for gasextraction; one or more sensors coupled to one or more pumps in themining well; wherein the one or more user equipment comprises of one ormore processors that execute a set of executable instructions that arestored in a memory, upon which execution, the processor causes thesystem to: acquire a set of data packets from one or more sensors, by adata acquisition engine, wherein the set of data packets are received atany synchronous and asynchronous instances of time; extract a set ofattributes, by a feature generation engine, from the synchronised datapackets, wherein the feature generation engine is configured to generatefeatures from the extracted set of attributes associated withinterpolation of the received data packets; evaluate, by a generativeadaptive network (GAN) engine, a set of model parameters based on thegenerated features of the extracted set of attributes; based on theevaluation of the set of model parameters, predict, by a predictionengine, failures associated with the received set of data packets. 2.The system as claimed in claim 1, wherein the mining well comprises anyoil, methane, coal bed or a combination thereof.
 3. The system asclaimed in claim 1, wherein the received data packets are synchronizedby converting the received data packets to a synchronous and regulartime interval data packets.
 4. The system as claimed in claim 1, whereinthe GAN engine is configured to detect anomalies associated with the setof model parameters.
 5. The system as claimed in claim 2, wherein theone or more user equipment comprises a SIM, wherein the SIM compriseselementary files and location parameters associated with the one or moreuser equipment.
 6. The system as claimed in claim 1, wherein a GANtraining engine is configured to monitor and update the one or moremodel parameters such that the system is configured to train itself toobtain the one or more model parameters to predict anomalies andgenerate failure data over a plurality of time interval.
 7. The systemas claimed in claim 1, wherein the GAN training engine is configured tolabel the failures by marking certain days data ahead of failure asbelonging to failure data.
 8. A method for facilitating prediction ofwear and tear and subsequent failure of components associated with gasextraction in a mining well, said method comprising: acquiring, by thedata acquisition engine, a set of data packets from one or more sensors,wherein the set of data packets are received at any synchronous andasynchronous instances of time; extracting, by a feature generationengine, a set of attributes from the acquired set of data packets,wherein the feature generation engine is configured to generate featuresfrom the extracted set of attributes associated with interpolation ofthe acquired data packets; evaluating, by a generative adaptive network(GAN) engine, a set of model parameters based on the generated featuresof the extracted set of attributes; based on the evaluation of the setof model parameters, predict, by a prediction engine, failuresassociated with the received set of data packets.
 9. The method asclaimed in claim 8, wherein the predicted failures are labelled andcategorised for efficient planning.
 10. The method as claimed in claim8, wherein noisy and missing data are identified, and whereininterpolation is used to estimate the noisy and missing data.
 11. Themethod as claimed in claim 8, wherein model parameters evaluated by theGAN engine corresponds to recall rate, precision and accuracy, whereinrecall rate is associated with number of failures detected correctly tototal number of actually occurred failures, wherein precision isassociated with number of failures detected correctly and total numberof failures detected.